from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel



@require_tf
class 							a				:







 """simple docstring"""






 lowerCamelCase      :List[str]            =						BlenderbotConfig
 lowerCamelCase      :Dict            =						{}
 lowerCamelCase      :str            =						'''gelu'''



 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_=13						,							lowerCAmelCase_=7						,							lowerCAmelCase_=True						,							lowerCAmelCase_=False						,							lowerCAmelCase_=99						,							lowerCAmelCase_=32						,							lowerCAmelCase_=2						,							lowerCAmelCase_=4						,							lowerCAmelCase_=37						,							lowerCAmelCase_=0.1						,							lowerCAmelCase_=0.1						,							lowerCAmelCase_=20						,							lowerCAmelCase_=2						,							lowerCAmelCase_=1						,							lowerCAmelCase_=0						,							)    ->   Tuple:
        _A	  =    parent
        _A	  =    batch_size
        _A	  =    seq_length
        _A	  =    is_training
        _A	  =    use_labels
        _A	  =    vocab_size
        _A	  =    hidden_size
        _A	  =    num_hidden_layers
        _A	  =    num_attention_heads
        _A	  =    intermediate_size

        _A	  =    hidden_dropout_prob
        _A	  =    attention_probs_dropout_prob
        _A	  =    max_position_embeddings
        _A	  =    eos_token_id
        _A	  =    pad_token_id
        _A	  =    bos_token_id



 def 	UpperCAmelCase				(   self				)    ->   Tuple:
        _A	  =    ids_tensor([self.batch_size, self.seq_length - 1]						,							self.vocab_size				)
        _A	  =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)						,							1				)
        _A	  =    tf.concat([input_ids, eos_tensor]						,							axis=1				)

        _A	  =    ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size				)

        _A	  =    self.config_cls(
            vocab_size=self.vocab_size						,							d_model=self.hidden_size						,							encoder_layers=self.num_hidden_layers						,							decoder_layers=self.num_hidden_layers						,							encoder_attention_heads=self.num_attention_heads						,							decoder_attention_heads=self.num_attention_heads						,							encoder_ffn_dim=self.intermediate_size						,							decoder_ffn_dim=self.intermediate_size						,							dropout=self.hidden_dropout_prob						,							attention_dropout=self.attention_probs_dropout_prob						,							max_position_embeddings=self.max_position_embeddings						,							eos_token_ids=[2]						,							bos_token_id=self.bos_token_id						,							pad_token_id=self.pad_token_id						,							decoder_start_token_id=self.pad_token_id						,							**self.config_updates						,							)
        _A	  =    prepare_blenderbot_inputs_dict(_a						,							_a						,							_a				)
        return config, inputs_dict



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_				)    ->   int:
        _A	  =    TFBlenderbotModel(config=_a				).get_decoder()
        _A	  =    inputs_dict["""input_ids"""]

        _A	  =    input_ids[:1, :]
        _A	  =    inputs_dict["""attention_mask"""][:1, :]
        _A	  =    inputs_dict["""head_mask"""]
        _A	  =    1

        # first forward pass
        _A	  =    model(_a						,							attention_mask=_a						,							head_mask=_a						,							use_cache=_a				)

        _A  ,    _A	  =    outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _A	  =    ids_tensor((self.batch_size, 3)						,							config.vocab_size				)
        _A	  =    tf.cast(ids_tensor((self.batch_size, 3)						,							2				)						,							tf.inta				)

        # append to next input_ids and
        _A	  =    tf.concat([input_ids, next_tokens]						,							axis=-1				)
        _A	  =    tf.concat([attention_mask, next_attn_mask]						,							axis=-1				)

        _A	  =    model(_a						,							attention_mask=_a				)[0]
        _A	  =    model(_a						,							attention_mask=_a						,							past_key_values=_a				)[0]

        self.parent.assertEqual(next_tokens.shape[1]						,							output_from_past.shape[1]				)

        # select random slice
        _A	  =    int(ids_tensor((1,)						,							output_from_past.shape[-1]				)				)
        _A	  =    output_from_no_past[:, -3:, random_slice_idx]
        _A	  =    output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(_a						,							_a						,							rtol=1E-3				)






def 					snake_case    (			snake_case__     :str  ,      snake_case__     :Dict  ,      snake_case__     :List[Any]  ,      snake_case__     :Optional[Any]=None  ,      snake_case__     :Dict=None  ,      snake_case__     :int=None  ,      snake_case__     :Optional[Any]=None  ,      snake_case__     :int=None  ,      )		->  Optional[int]:
       if attention_mask is None:
              _A	  =    tf.cast(tf.math.not_equal(snake_case__  ,      config.pad_token_id)  ,      tf.inta)
       if decoder_attention_mask is None:
              _A	  =    tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape  ,      dtype=tf.inta),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,      config.pad_token_id)  ,      tf.inta),
                  ]  ,      axis=-1  ,      )
       if head_mask is None:
              _A	  =    tf.ones((config.encoder_layers, config.encoder_attention_heads))
       if decoder_head_mask is None:
              _A	  =    tf.ones((config.decoder_layers, config.decoder_attention_heads))
       if cross_attn_head_mask is None:
              _A	  =    tf.ones((config.decoder_layers, config.decoder_attention_heads))
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }



@require_tf
class 							a				(     UpperCAmelCase__			,     UpperCAmelCase__			,     unittest.TestCase			):







 """simple docstring"""






 lowerCamelCase      :str            =						(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
 lowerCamelCase      :Tuple            =						(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
 lowerCamelCase      :Union[str, Any]            =						(
     {
         '''conversational''': TFBlenderbotForConditionalGeneration,
         '''feature-extraction''': TFBlenderbotModel,
         '''summarization''': TFBlenderbotForConditionalGeneration,
         '''text2text-generation''': TFBlenderbotForConditionalGeneration,
         '''translation''': TFBlenderbotForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 lowerCamelCase      :Any            =						True
 lowerCamelCase      :Optional[int]            =						False
 lowerCamelCase      :str            =						False



 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        _A	  =    TFBlenderbotModelTester(self				)
        _A	  =    ConfigTester(self						,							config_class=_a				)



 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        self.config_tester.run_common_tests()



 def 	UpperCAmelCase				(   self				)    ->   List[Any]:
        _A	  =    self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*_a				)



@require_tokenizers
@require_tf
class 							a				(     unittest.TestCase			):







 """simple docstring"""






 lowerCamelCase      :List[Any]            =						['''My friends are cool but they eat too many carbs.''']
 lowerCamelCase      :Tuple            =						'''facebook/blenderbot-400M-distill'''



 @cached_property
 def 	UpperCAmelCase				(   self				)    ->   Dict:
        return BlenderbotTokenizer.from_pretrained(self.model_name				)



 @cached_property
 def 	UpperCAmelCase				(   self				)    ->   List[str]:
        _A	  =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
        return model



 @slow
 def 	UpperCAmelCase				(   self				)    ->   int:
        _A	  =    self.tokenizer(self.src_text						,							return_tensors="""tf"""				)
        _A	  =    self.model.generate(
            model_inputs.input_ids						,							)
        _A	  =    self.tokenizer.batch_decode(generated_ids.numpy()						,							skip_special_tokens=_a				)[0]
        assert (
            generated_words
            == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
        )










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase				:     Optional[int]					=				{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase				:     int					=				[
					    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SwinForImageClassification""",
					    """SwinForMaskedImageModeling""",
					    """SwinModel""",
					    """SwinPreTrainedModel""",
					    """SwinBackbone""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase				:     str					=				[
					    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFSwinForImageClassification""",
					    """TFSwinForMaskedImageModeling""",
					    """TFSwinModel""",
					    """TFSwinPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swin import (
										    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinBackbone,
										    SwinForImageClassification,
										    SwinForMaskedImageModeling,
										    SwinModel,
										    SwinPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_swin import (
										    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSwinForImageClassification,
										    TFSwinForMaskedImageModeling,
										    TFSwinModel,
										    TFSwinPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase				:     int					=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



